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Topic Detection for Online Course Feedback Using LDA

  • Sayan UnankardEmail author
  • Wanvimol Nadee
Conference paper
  • 6 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11984)

Abstract

In an online course, student feedback is used widely in order to enhance the quality of teaching and learning process by improving the teacher-student relationship. If a lecturer wants to get a summary of these comments, the lecturer has to manually read and summarize all these comments. However, dealing with a very large number of comments is difficult. In this paper, we proposed an approach for topic detection for online course feedback by adopting Latent Dirichlet Allocation (LDA). The course feedback from the website of Coursera (i.e., Machine Learning course) is used to demonstrate the effectiveness of our approach.

Keywords

Course feedback Online learning Topic detection LDA 

References

  1. 1.
    Allahyari, M., et al.: Text summarization techniques: a brief survey. arXiv preprint arXiv:1707.02268 (2017)
  2. 2.
    Alvanaki, F., Michel, S., Ramamritham, K., Weikum, G.: See what’s enblogue: real-time emergent topic identification in social media. In: Proceedings of 15th International Conference on Extending Database Technology, EDBT 2012, Berlin, Germany, 27–30 March 2012, pp. 336–347 (2012)Google Scholar
  3. 3.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012).  https://doi.org/10.1145/2133806.2133826CrossRefGoogle Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(1), 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Bonnel, W.: Improving feedback to students in online courses. Nurs. Educ. Perspect. 29(5), 290–294 (2008)Google Scholar
  6. 6.
    Cataldi, M., Caro, L.D., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, pp. 4:1–4:10 (2010)Google Scholar
  7. 7.
    Chathuranga, J., Ediriweera, S., Hasantha, R., Munasinghe, P., Ranathunga, S.: Annotating opinions and opinion targets in student course feedback. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) (2018)Google Scholar
  8. 8.
    Cohen, R., Ruths, D.: Classifying political orientation on twitter: it’s not easy!. In: Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013, pp. 91–99, January 2013Google Scholar
  9. 9.
    Cristani, M., Perina, A., Castellani, U., Murino, V.: Geo-located image analysis using latent representations. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008Google Scholar
  10. 10.
    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1277–1287. EMNLP 2010. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1870658.1870782
  11. 11.
    Fan, X., Luo, W., Menekse, M., Litman, D., Wang, J.: Coursemirror: enhancing large classroom instructor-student interactions via mobile interfaces and natural language processing. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1473–1478. ACM (2015)Google Scholar
  12. 12.
    Fang, Y., Si, L., Somasundaram, N., Yu, Z.: Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 63–72. WSDM 2012. ACM, New York (2012). http://doi.acm.org/10.1145/2124295.2124306
  13. 13.
    García-Hernández, R.A., Montiel, R., Ledeneva, Y., Rendón, E., Gelbukh, A., Cruz, R.: Text summarization by sentence extraction using unsupervised learning. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 133–143. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88636-5_12CrossRefGoogle Scholar
  14. 14.
    Goorha, S., Ungar, L.H.: Discovery of significant emerging trends. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 25–28 July 2010, pp. 57–64 (2010)Google Scholar
  15. 15.
    Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers. pp. 1262–1273 (2014)Google Scholar
  16. 16.
    Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: Proceedings of the First Workshop on Social Media Analytics. SOMA 2010, pp. 80–88. ACM, New York (2010). http://doi.acm.org/10.1145/1964858.1964870
  17. 17.
    Paul, M.J., Dredze, M.: You are what your tweet: analyzing twitter for public health. Artif. Intell. 38, 265–272 (2011)Google Scholar
  18. 18.
    Linstead, E., Rigor, P., Bajracharya, S., Lopes, C., Baldi, P.: Mining concepts from code with probabilistic topic models. In: Proceedings of the Twenty-second IEEE/ACM International Conference on Automated Software Engineering. ASE 2007, pp. 461–464. ACM, New York (2007). http://doi.acm.org/10.1145/1321631.1321709
  19. 19.
    Lumpkin, A., Achen, R.M., Dodd, R.K.: Student perceptions of active learning. Coll. Stud. J. 49(1), 121–133 (2015)Google Scholar
  20. 20.
    Luo, W., Fan, X., Menekse, M., Wang, J., Litman, D.: Enhancing instructor-student and student-student interactions with mobile interfaces and summarization. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 16–20 (2015)Google Scholar
  21. 21.
    Luo, W., Litman, D.: Summarizing student responses to reflection prompts. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1955–1960 (2015)Google Scholar
  22. 22.
    Luo, W., Liu, F., Litman, D.: An improved phrase-based approach to annotating and summarizing student course responses. arXiv preprint arXiv:1805.10396 (2018)
  23. 23.
    Luo, W., Liu, F., Liu, Z., Litman, D.: Automatic summarization of student course feedback. arXiv preprint arXiv:1805.10395 (2018)
  24. 24.
    Martins, A.F., Smith, N.A.: Summarization with a joint model for sentence extraction and compression. In: Proceedings of the Workshop on Integer Linear Programming for Natural Language Processing, pp. 1–9. Association for Computational Linguistics (2009)Google Scholar
  25. 25.
    Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, 6–10 June 2010, pp. 1155–1158 (2010)Google Scholar
  26. 26.
    Mehrotra, R., Sanner, S., Buntine, W., Xie, L.: Improving LDA topic models for microblogs via tweet pooling and automatic labeling. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 889–892. ACM (2013)Google Scholar
  27. 27.
    Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)Google Scholar
  28. 28.
    Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)
  29. 29.
    Schinas, M., Papadopoulos, S., Kompatsiaris, Y., Mitkas, P.A.: Visual event summarization on social media using topic modelling and graph-based ranking algorithms. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 203–210. ACM (2015)Google Scholar
  30. 30.
    Shapiro, H.B., Lee, C.H., Roth, N.E.W., Li, K., Rundel, M.Ç., Canelas, D.A.: Understanding the massive open online course (MOOC) student experience: an examination of attitudes motivations and barriers. Comput. Educ. 110, 35–50 (2017)CrossRefGoogle Scholar
  31. 31.
    Steyn, C., Davies, C., Sambo, A.: Eliciting student feedback for course development: the application of a qualitative course evaluation tool among business research students. Assess. Eval. High. Educ. 44(1), 11–24 (2019)CrossRefGoogle Scholar
  32. 32.
    Sung, Y.T., Liao, C.N., Chang, T.H., Chen, C.L., Chang, K.E.: The effect of online summary assessment and feedback system on the summary writing on 6th graders: The LSA-based technique. Compu. Educ. 95, 1–18 (2016)CrossRefGoogle Scholar
  33. 33.
    Thomas, S.W.: Mining software repositories using topic models. In: 2011 33rd International Conference on Software Engineering (ICSE), pp. 1138–1139, May 2011Google Scholar
  34. 34.
    Toven-Lindsey, B., Rhoads, R.A., Lozano, J.B.: Virtually unlimited classrooms: pedagogical practices in massive open online courses. Internet High. Educ. 24, 1–12 (2015)CrossRefGoogle Scholar
  35. 35.
    Wan, X., Yang, J.: Multi-document summarization using cluster-based link analysis. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 299–306. ACM (2008)Google Scholar
  36. 36.
    Wang, L., Ling, W.: Neural network-based abstract generation for opinions and arguments. arXiv preprint arXiv:1606.02785 (2016)
  37. 37.
    Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 123–131. ACM (2012)Google Scholar
  38. 38.
    Welch, C., Mihalcea, R.: Targeted sentiment to understand student comments. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2471–2481 (2016)Google Scholar
  39. 39.
    Xiong, W., Litman, D.: Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews. In: Proceedings of coling 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 1985–1995 (2014)Google Scholar
  40. 40.
    Zhang, Y., Chen, M., Huang, D., Wu, D., Li, Y.: iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener. Comput. Syst. 66, 30–35 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Information Technology Division, Faculty of ScienceMaejo UniversityChiang MaiThailand

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